Multi-agent Coordination Algorithms for Pursuit-Evasion

Sun, Lijun

Multi-agent Coordination Algorithms for Pursuit-Evasion

Sun, Lijun

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Publication Type:: Thesis
Issue Date:: 2023

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Field	Value	Language
dc.contributor.author	Sun, Lijun
dc.date.accessioned	2024-03-26T00:20:46Z
dc.date.available	2024-03-26T00:20:46Z
dc.date.issued	2023
dc.identifier.uri	http://hdl.handle.net/10453/177149
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_US.UTF-8
dc.description.abstract	The real-world application of multi-agent systems (MAS) is still challenging and limited, despite the exclusive advantage of MAS over single agent systems. The multi-agent coordination or swarm intelligence is a paramount concern. Based on pursuit-evasion games, this thesis investigates the safe distributed implicit coordination of large-scale multi-agent systems, as some self-organizing systems in nature. Particularly, five research questions are answered in the thesis: (1) how to resolve collisions in the multi-agent environment; (2) how to be cooperative in a distributed way; (3) how to allocate the multi-agent task; (4) how to overcome the partial observation; and (5) how to get the arms race from the adversarial co-evolution. Accordingly, a safety-constrained multi-agent pursuit-evasion platform: MatrixWorld is proposed, and three coordination algorithms are designed, i.e., the cooperative coevolutionary particle swarm optimization (CCPSO-R) algorithm, the biquadratic assignment problem solver and parallel CCPSO-R (BiPCCR) algorithm, and the fuzzy self-organizing cooperative coevolution (FSC2) algorithm. Furthermore, the co-evolution mechanism, autocurricula, self-play, arms races, and adversarial learning are explored in the multi-agent setting by literature review and experimental verification. Safe coordination of thousands of agents is achieved in the experiments.	en_US.UTF-8
dc.format	Thesis (PhD)
dc.language.iso	en_US	en_US.UTF-8
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/177149/1/thesis.pdf
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	© 2023 Lijun Sun
dc.rights	au.edu.uts.lib/cph
dc.title	Multi-agent Coordination Algorithms for Pursuit-Evasion	en_US.UTF-8
dc.type	Thesis
utslib.copyright.status	open_access	*

Abstract:

The real-world application of multi-agent systems (MAS) is still challenging and limited, despite the exclusive advantage of MAS over single agent systems. The multi-agent coordination or swarm intelligence is a paramount concern. Based on pursuit-evasion games, this thesis investigates the safe distributed implicit coordination of large-scale multi-agent systems, as some self-organizing systems in nature. Particularly, five research questions are answered in the thesis: (1) how to resolve collisions in the multi-agent environment; (2) how to be cooperative in a distributed way; (3) how to allocate the multi-agent task; (4) how to overcome the partial observation; and (5) how to get the arms race from the adversarial co-evolution. Accordingly, a safety-constrained multi-agent pursuit-evasion platform: MatrixWorld is proposed, and three coordination algorithms are designed, i.e., the cooperative coevolutionary particle swarm optimization (CCPSO-R) algorithm, the biquadratic assignment problem solver and parallel CCPSO-R (BiPCCR) algorithm, and the fuzzy self-organizing cooperative coevolution (FSC2) algorithm. Furthermore, the co-evolution mechanism, autocurricula, self-play, arms races, and adversarial learning are explored in the multi-agent setting by literature review and experimental verification. Safe coordination of thousands of agents is achieved in the experiments.

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http://hdl.handle.net/10453/177149